Method and device for automatically extracting relation between knowledge graph events in economic field

Abstract

The invention provides a method and device for automatically extracting relation beteween knowledge graph events in an economic field , a storage medium and a processor. The method comprises the steps: obtaining original data, wherein the original data is data in the field of economy and finance; according to the event knowledge graph infrastructure and the original data, constructing an event knowledge graph, wherein the event knowledge graph comprises theme events; sorting the theme events according to time; grouping the sorted theme events into a plurality of transactions according to a preset time window, wherein at least one transaction comprises the theme events; based on the affairs, mining association rules by adopting a frequent item set mining algorithm; and determining an association relation of the mined association rules by adopting a machine learning algorithm, wherein the association relation is an event relation. According to the method, the association rules are minedthrough a frequent item set mining algorithm, then the association relation of the mined association rules is determined through a machine learning algorithm, and compared with a judgment method in the prior art, the method can achieve rapid judgment of the event relation.

Description

technical field [0001] The present application relates to the economic field, and in particular, to a method, device, storage medium and processor for automatically extracting event relationships from knowledge graphs in the economic field. Background technique [0002] In the current knowledge graph field, from the application point of view, the static entity-relationship or entity-attribute-value knowledge graph architecture cannot meet the increasingly complex needs, especially in the field of economics and finance. The economic and financial fields need to capture external events in a timely manner, and conduct sand table deductions and event warnings based on the causal, succession, reversal, and conditions between events. Existing static entity knowledge graph models cannot meet this requirement. The event knowledge graph can establish complex associations between events and events, events and entities, and this relationship as well as entity attributes and event attr...

AI Technical Summary

Problems solved by technology

[0005] The main purpose of this application is to provide a method, device, storage medium, and processor for automatically extracting event relations from knowledge graphs in the economic field, so as to solve the problem of low efficiency in determining the relationship between events in event knowledge graphs in the prior art

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